Powerful Insurance Claims Rejection Predictive Model






Efficiency increase in accuracy of insurance claims rejection prediction

A unique method to gauge the chances of an insurance claim being rejected

Health insurance plans offer comprehensive security when it comes to extensive medical-related expenditures. Yet, when filing an insurance claim for any service, various reasons may lead to a rejection of the claim, ushering in a financial burden on the client and a loss of monetary compensation for the service provider. Thus, to avoid potential embarrassment and payment lapses, healthcare organizations claiming insurance require a mechanism to forecast their clients’ eligibility.

We were approached by a California-based client who had created the world’s first cross-functional revenue intelligence software. The massive scope of the product required a sophisticated fusion of data engineering services and machine learning expertise. The client approached us to bolster their existing product with cutting-edge analytical functionality.


Data engineering, Data analysis, and Web development


Python Django, AWS RDS, PostgreSQL, Docker, Jenkins, AWS Redshift


Palo Alto, California, USA

The roadblocks we had to break through

  • Applying a comprehensive and systematic approach to the predictive analysis of insurance claims was necessary, which necessitated in-depth familiarity with US healthcare’s revenue cycle management.
  • The study used machine learning models to forecast whether an insurance provider will approve or deny a claim. As a result, it required in-depth knowledge of machine learning and data engineering, which is a complex combination to find.
  • The revenue intelligence software automated the creation, ingestion, devalidation, and monitoring of the rules for making predictions. This meant that the modules to be implemented had to be very precise and detailed.

Solutions we enforced to the aforesaid challenges

Our experts have worked extensively with parties related to the healthcare industry and hence are proficient in tasks dealing with healthcare, thus, this was a fairly workable situation for us. We approached the project with the primary aim of improving claim acceptance and rejection prediction.


  • As part of the assignment, we created a module defining rules based on which the software bases its predictions. These rules relied on the extensive examination of insurance data related to claims and denial of claims. For every anticipated claim denial, alternate suggestions were also provided.
  • Modules verifying claims made and automating the workflow process were also integrated.
  • The unique module developed was the devalidator that made away with irrelevant rules and allowed new rules to be incorporated in the predictive modeling.


Handling a cross-functional team meant effective management of the members and the tasks involved. Accordingly, the project work was divided into sprints. Feedback sessions were held to discuss project progress and make necessary changes based on new requirements if any.

Modules implemented

Rules Creator

This module used data from the AWS Redshift warehouse to train the revenue intelligence model periodically. As a result, rules were created. Each rule resulted from a thorough examination of the claims and the material submitted indicating claim denial. For each sort of claim refused, the rules generator also produced suggestions.

Rules Runner

This module was implemented to read the rules created by the rules creator to check if the rule existed in the database. If a rule was new, it was ingested into the database.

Rules Automator

This master script automated the rule-creation and rule-ingestion procedure, and the algorithm ran the show. At each stage of the process, the algorithm sent Slack notifications so that they could be remotely monitored from any device without a console or dashboard.

Claims Validator

This tool was used to validate claims. New validators could also be rapidly integrated into this service, enhancing the tool’s scalability and maintainability.

Rule Devalidator

Rules evolve throughout time; therefore, the product needed to recognize those that were no longer relevant. Thus, this form of predictive modeling enhanced the revenue intelligence software by revalidating those rules.

High Level Design Architecture

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The project introduced an intensive outlook to the entire insurance game

The client had clear expectations for improving their existing machine-learning models. Thus the assignment challenged our experts to put their best foot forward. The intensive knowledge of machine learning and the creation of the prediction model worked in the best interest of both parties. Our team performed effectively and beyond expectations.


The modules implemented allowed healthcare professionals to carry out rejection predictions with accuracy. The revenue intelligence software boosted patient engagement through accessible treatment options, decreased cash flow cycles through intelligent denial management, and raised revenues through fewer rejections.


This project necessitated periodic revisions because the healthcare sector constantly changes, impacting insurance policies. Based on fresh modifications and specifications, our team of professionals has been working on updating the product.

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